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// Copyright Vespa.ai. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
package com.yahoo.vespa.indexinglanguage.expressions;
import com.yahoo.document.ArrayDataType;
import com.yahoo.document.DataType;
import com.yahoo.document.DocumentType;
import com.yahoo.document.Field;
import com.yahoo.document.TensorDataType;
import com.yahoo.document.datatypes.Array;
import com.yahoo.document.datatypes.StringFieldValue;
import com.yahoo.document.datatypes.TensorFieldValue;
import com.yahoo.language.process.Embedder;
import com.yahoo.tensor.Tensor;
import com.yahoo.tensor.TensorType;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import java.util.Objects;
/**
* Embeds a string in a tensor space using the configured Embedder component
*
* @author bratseth
*/
public class EmbedExpression extends Expression {
private final Embedder embedder;
private final String embedderId;
private final List<String> embedderArguments;
/** The destination the embedding will be written to on the form [schema name].[field name] */
private String destination;
/** The target type we are embedding into. */
private TensorType targetType;
public EmbedExpression(Map<String, Embedder> embedders, String embedderId, List<String> embedderArguments) {
super(null);
this.embedderId = embedderId;
this.embedderArguments = List.copyOf(embedderArguments);
boolean embedderIdProvided = embedderId != null && !embedderId.isEmpty();
if (embedders.size() == 0) {
throw new IllegalStateException("No embedders provided"); // should never happen
}
else if (embedders.size() == 1 && ! embedderIdProvided) {
this.embedder = embedders.entrySet().stream().findFirst().get().getValue();
}
else if (embedders.size() > 1 && ! embedderIdProvided) {
this.embedder = new Embedder.FailingEmbedder("Multiple embedders are provided but no embedder id is given. " +
"Valid embedders are " + validEmbedders(embedders));
}
else if ( ! embedders.containsKey(embedderId)) {
this.embedder = new Embedder.FailingEmbedder("Can't find embedder '" + embedderId + "'. " +
"Valid embedders are " + validEmbedders(embedders));
} else {
this.embedder = embedders.get(embedderId);
}
}
@Override
public void setStatementOutput(DocumentType documentType, Field field) {
targetType = toTargetTensor(field.getDataType());
destination = documentType.getName() + "." + field.getName();
}
@Override
protected void doExecute(ExecutionContext context) {
if (context.getValue() == null) return;
Tensor output;
if (context.getValue().getDataType() == DataType.STRING) {
output = embedSingleValue(context);
}
else if (context.getValue().getDataType() instanceof ArrayDataType &&
((ArrayDataType)context.getValue().getDataType()).getNestedType() == DataType.STRING) {
output = embedArrayValue(context);
}
else {
throw new IllegalArgumentException("Embedding can only be done on string or string array fields, not " +
context.getValue().getDataType());
}
context.setValue(new TensorFieldValue(output));
}
private Tensor embedSingleValue(ExecutionContext context) {
StringFieldValue input = (StringFieldValue)context.getValue();
return embed(input.getString(), targetType, context);
}
@SuppressWarnings("unchecked")
private Tensor embedArrayValue(ExecutionContext context) {
var input = (Array<StringFieldValue>)context.getValue();
var builder = Tensor.Builder.of(targetType);
if (targetType.rank() == 2)
embedArrayValueToRank2Tensor(input, builder, context);
else
embedArrayValueToRank3Tensor(input, builder, context);
return builder.build();
}
private void embedArrayValueToRank2Tensor(Array<StringFieldValue> input,
Tensor.Builder builder,
ExecutionContext context) {
String mappedDimension = targetType.mappedSubtype().dimensions().get(0).name();
String indexedDimension = targetType.indexedSubtype().dimensions().get(0).name();
for (int i = 0; i < input.size(); i++) {
Tensor tensor = embed(input.get(i).getString(), targetType.indexedSubtype(), context);
for (Iterator<Tensor.Cell> cells = tensor.cellIterator(); cells.hasNext(); ) {
Tensor.Cell cell = cells.next();
builder.cell()
.label(mappedDimension, i)
.label(indexedDimension, cell.getKey().numericLabel(0))
.value(cell.getValue());
}
}
}
private void embedArrayValueToRank3Tensor(Array<StringFieldValue> input,
Tensor.Builder builder,
ExecutionContext context) {
String outerMappedDimension = embedderArguments.get(0);
String innerMappedDimension = targetType.mappedSubtype().dimensionNames().stream().filter(d -> !d.equals(outerMappedDimension)).findFirst().get();
String indexedDimension = targetType.indexedSubtype().dimensions().get(0).name();
long indexedDimensionSize = targetType.indexedSubtype().dimensions().get(0).size().get();
var innerType = new TensorType.Builder(targetType.valueType()).mapped(innerMappedDimension).indexed(indexedDimension,indexedDimensionSize).build();
int innerMappedDimensionIndex = innerType.indexOfDimensionAsInt(innerMappedDimension);
int indexedDimensionIndex = innerType.indexOfDimensionAsInt(indexedDimension);
for (int i = 0; i < input.size(); i++) {
Tensor tensor = embed(input.get(i).getString(), innerType, context);
for (Iterator<Tensor.Cell> cells = tensor.cellIterator(); cells.hasNext(); ) {
Tensor.Cell cell = cells.next();
builder.cell()
.label(outerMappedDimension, i)
.label(innerMappedDimension, cell.getKey().label(innerMappedDimensionIndex))
.label(indexedDimension, cell.getKey().numericLabel(indexedDimensionIndex))
.value(cell.getValue());
}
}
}
private Tensor embed(String input, TensorType targetType, ExecutionContext context) {
return embedder.embed(input,
new Embedder.Context(destination).setLanguage(context.getLanguage()).setEmbedderId(embedderId),
targetType);
}
@Override
protected void doVerify(VerificationContext context) {
String outputField = context.getOutputField();
if (outputField == null)
throw new VerificationException(this, "No output field in this statement: " +
"Don't know what tensor type to embed into");
targetType = toTargetTensor(context.getInputType(this, outputField));
if ( ! validTarget(targetType))
throw new VerificationException(this, "The embedding target field must either be a dense 1d tensor, a mapped 1d tensor," +
"an array of dense 1d tensors, or a mixed 2d or 3d tensor");
if (targetType.rank() == 3) {
if (embedderArguments.size() != 1)
throw new VerificationException(this, "When the embedding target field is a 3d tensor " +
"the name of the tensor dimension that corresponds to the input array elements must " +
"be given as a second argument to embed, e.g: ... | embed colbert paragraph | ...");
if ( ! targetType.mappedSubtype().dimensionNames().contains(embedderArguments.get(0)))
throw new VerificationException(this, "The dimension '" + embedderArguments.get(0) + "' given to embed " +
"is not a sparse dimension of the target type " + targetType);
}
context.setValueType(createdOutputType());
}
@Override
public DataType createdOutputType() {
return new TensorDataType(targetType);
}
private static TensorType toTargetTensor(DataType dataType) {
if (dataType instanceof ArrayDataType) return toTargetTensor(((ArrayDataType) dataType).getNestedType());
if ( ! ( dataType instanceof TensorDataType))
throw new IllegalArgumentException("Expected a tensor data type but got " + dataType);
return ((TensorDataType)dataType).getTensorType();
}
private boolean validTarget(TensorType target) {
if (target.rank() == 1) // indexed or mapped 1d tensor
return true;
if (target.rank() == 2 && target.indexedSubtype().rank() == 1)
return true; // mixed 2d tensor
if (target.rank() == 3 && target.indexedSubtype().rank() == 1)
return true; // mixed 3d tensor
return false;
}
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("embed");
if (this.embedderId != null && !this.embedderId.isEmpty())
sb.append(" ").append(this.embedderId);
embedderArguments.forEach(arg -> sb.append(" ").append(arg));
return sb.toString();
}
@Override
public int hashCode() { return EmbedExpression.class.hashCode(); }
@Override
public boolean equals(Object o) {
return o instanceof EmbedExpression;
}
private static String validEmbedders(Map<String, Embedder> embedders) {
List<String> embedderIds = new ArrayList<>();
embedders.forEach((key, value) -> embedderIds.add(key));
embedderIds.sort(null);
return String.join(",", embedderIds);
}
}
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